The present invention relates to the field of digital imaging and image enhancement. More particularly, the invention relates to a technique for improving image enhancement by appropriately filtering input image data based upon an optimal sampling rate for the data.
Many techniques are currently available and in use for creating digital images. The techniques range in complexity from simple photographic techniques to much more complex imaging modalities such as those used in medical imaging, part inspection, parcel and baggage inspection, and so forth. More complex imaging modalities include computed tomography (CT) imaging systems, magnetic resonance imaging (MRI) systems, digital X-ray imaging systems, and so forth. In all of these applications there is a continuing need for improvement of the resolution and clarity of images produced. Generally speaking, these improvements are made in post-processing steps in which the image data is manipulated through various calculations to enhance the clarity and general usefulness of the final reconstructed image.
Current imaging systems produce images of differing spatial resolutions. A parameter known as the point-spread function of the systems is largely dependent upon imaging parameters, and results in such differing spatial resolutions. In CT images, for example, the particular reconstruction algorithm used to convert the acquired data to image data primarily determines the extent of the point-spread function. In MRI systems, similarly, the amount of zero-filled interpolation in the k-space data (the data acquired during an imaging sequence) affects the spatial extent of the point-spread function. In X-ray systems, the distance between the source and detector determines the point-spread function of the system.
Image filtering algorithms aimed at removing random noise from images do not currently account for this variation in the inherent spatial resolution. Accordingly, such algorithms perform sub-optimally in terms of image quality. In such frameworks, a loss of spatial resolution in the filtered image often results, even as they may successfully reduce levels of random noise.
There is a need, therefore, for an improved technique for filtering digital images. There is, at present, a particular need for a technique that can be used in different contexts and to account for a different point-spread function bases.